A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation Imagery
This addresses a domain-specific problem for geophysical studies by improving semantic segmentation accuracy in satellite imagery, but it is incremental as it builds on existing tiling and augmentation methods.
They tackled the problem of preserving spatial context in Earth observation imagery for semantic segmentation when object locations are unknown, and found that their Flip-n-Slide tiling strategy outperforms previous state-of-the-art methods, increasing precision by up to 15.8% for underrepresented classes.
We propose a new tiling strategy, Flip-n-Slide, which has been developed for specific use with large Earth observation satellite images when the location of objects-of-interest (OoI) is unknown and spatial context can be necessary for class disambiguation. Flip-n-Slide is a concise and minimalistic approach that allows OoI to be represented at multiple tile positions and orientations. This strategy introduces multiple views of spatio-contextual information, without introducing redundancies into the training set. By maintaining distinct transformation permutations for each tile overlap, we enhance the generalizability of the training set without misrepresenting the true data distribution. Our experiments validate the effectiveness of Flip-n-Slide in the task of semantic segmentation, a necessary data product in geophysical studies. We find that Flip-n-Slide outperforms the previous state-of-the-art augmentation routines for tiled data in all evaluation metrics. For underrepresented classes, Flip-n-Slide increases precision by as much as 15.8%.